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Stop paying gpt-4o prices for tasks gpt-4o-mini handles just as well. Three working approaches to automatic complexity routing: heuristics, classifier calls, an

Eval suites are snapshots. Production is a stream. The failures that matter most are the ones your evals weren't written to look for.

Token spend is the visible cost. Retries, failed calls, and over-provisioned models for simple tasks are where the real money goes. Here's how to measure and re

Single-agent failures are isolated. Multi-agent failures compound. Here's how to instrument a 3-agent pipeline so you can actually debug it when things go wrong

OpenClaw defaults to one model for everything. Here's how to wire Kalibr so your agent automatically routes heartbeat checks to cheap models and complex reasoni

Manual try/except fallback chains are fragile and static. Here's how Thompson Sampling routes between LLM paths based on real outcome signals — with CrewAI and

A no-nonsense checklist for Python AI agents going to production. Error handling, retries, fallbacks, outcome tracking, cost monitoring — and how the pieces act